stochastic.fi: Stochastic generalized fragility indices

In brb225/FragilityTools: Toolbox For Patient Count Fragility Measures

Description

This function calculates the stochastic generalized fragility indices, which are a stochastic version of the generalized fragility indices. They ensure that a random collection of patients can reverse significance with sufficiently high probability, thus ensuring that typical patients can reverse significance.

Usage

stochastic.fi(
  X,
  Y,
  get.replacements,
  get.p.val,
  r = 0.5,
  nsim = 10,
  qfi.init = 10L,
  alpha = 0.05,
  verbose = FALSE,
  cl = NULL,
  D = 20,
  gamma = 0.2,
  init.step = TRUE
)

Arguments

X	a data frame of covariates which are not subject to modification.
Y	a data frame of responses which are subject to modification.
get.replacements	a function which outputs a data frame containing all possible row replacements for Y which are to be considered. The functions inputs the row of Y under consideration, the row of X under consideration, the row name, and the full original data frames X and Y.
get.p.val	a function that inputs X and Y and returns a p value
r	the index of the stochastic fragility index, by default 0.5. Having r=0 is equivalent to the generalized fragility index and having r=1 means that all patient combinations of the output size can reverse significance.
nsim	The number of simulations in the root finding algorithm, by default 10
alpha	a numeric for the significance cutoff
verbose	a logical value for whether to print status updates while running
cl	a cluster from the parallel package, used to compute fragility index over each modified observation at each stage of the greedy algorithm
D	a parameter for Polyak-Ruppert averaging, by default 20
gamma	a parameter for Polyak-Ruppert averaging, by default 0.2
init.step	a parameter for Polyak-Ruppert averaging, by default TRUE
gfi.init	An initialization of the output size, by default 10

Value

a length 2 list, with the first entry giving the stochastic generalized fragility index and the last entry giving the history of the root finding algorithm.

Examples

n <- 100
X <- data.frame("tr_group" = sample(c("treated", "not treated"), n, TRUE))
Y <- data.frame("outcome" = sample(c("sick", "healthy"), n, TRUE))
get.p.val <- function(X, Y) fisher.test(table(X[[1]], Y[[1]]))$p.value
get.replacements <- function(y, x, rn, Y, X) data.frame(Y=setdiff(unique(Y[[1]]), y))

stochastic.fi(X, Y, get.replacements, get.p.val)

brb225/FragilityTools documentation built on Jan. 21, 2022, 1:26 a.m.